Abstract

1. Species' population trends are fundamental to conservation, underpinning IUCN red-list classifications, many national lists of threatened species and are used globally to convey to policy makers the state of nature. Clearly, it's crucial to quantify how much we can trust population trend data, yet many studies analysing large numbers of population time series lack a straightforward way to do so. 2. Here we present a novel method that artificially degrades high quality population trend data to see how often subsets of the data, taken according to different sampling methods, correctly estimate the direction and magnitude of each population's complete trend. We test our method on a dataset of 27,930 waterbird population time series from across North America. 3. We find that if a significant trend is detected, even from only a small subset of years, it is likely to be representative of the complete trend. But you need to sample for many years to be confident that an insignificant trend (indicating a stable population) is correct, and it is more likely to be missing a decline than an increase in a population. 4. Our methods provide a clear and quantitative way to assign confidence to species trends, which will enable rigour to be added to large-scale population analyses, and can facilitate planning of future monitoring schemes. Our methods are readily applicable to other taxa and we provide the tools to do so.

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